Description

This paper addresses the problem of building concise, diverse and relevant lists of documents, which can be recommended to the participants of a conversation to fulfill their information needs without distracting them. These lists are retrieved periodically by submitting multiple implicit queries derived from the pronounced words. Each query is related to one of the topics identified in the conversation fragment preceding the recommendation, and is submitted to a search engine over the English Wikipedia. We propose in this paper an algorithm for diverse merging of these lists, using a sub modular reward function that rewards the topical similarity of documents to the conversation words as well as their diversity. We evaluate the proposed method through crowdsourcing. The results show the superiority of the diverse merging technique over several others which not enforce the diversity of topics. In this paper, we introduce a novel keyword extraction technique from ASR output, which maximizes the coverage of potential information needs of users and reduces the number of irrelevant words. Once a set of keywords is extracted, it is clustered in order to build several topically- separated queries, which are run independently, offering better precision than a larger, topically-mixed query. Results are finally merged in to a ranked set before showing them as recommendations to users.